The data life cycle
- Definition — what the data element means and how it's structured
- Capture / creation — controls at the point of entry matter most here
- Storage — encryption, classification, retention
- Usage / processing — access control, audit logging
- Sharing / transmission — encryption in transit, third-party agreements
- Archival — retention schedules, retrievability
- Destruction — secure, documented, irreversible disposal
Dimensions of data quality
| Dimension | Question it answers |
|---|---|
| Completeness | Is anything missing? |
| Accuracy | Does it reflect reality? |
| Consistency | Does it agree across systems? |
| Timeliness | Is it current enough to be useful? |
| Validity | Does it conform to defined formats and rules? |
| Uniqueness | Are there duplicates? |
Governance roles
- Data owner — a business leader accountable for a data domain; approves access and classification
- Data steward — manages quality, definitions, and standards day to day
- Data custodian — IT function that stores and safeguards the data technically
- Data user — consumes data within approved rules
IMPORTANT: Governance separates accountability (owner decides who may access and how data is classified) from custody (IT implements and protects). Blurring these — for example, letting IT decide who gets access to payroll data — is a governance weakness.
Master data management
MDM creates a single authoritative record for core entities — customers, vendors, products, employees — reducing duplicates and conflicting values across systems. Poor master data is a common root cause of duplicate vendor payments and unreliable reporting.
EXAM TIP: ETL (Extract, Transform, Load) moves data into a warehouse with transformation before loading; ELT loads raw data first and transforms it later inside the target platform. Either way, data validation during transformation is a key control point.